{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "a040718f-95ed-4e39-8e96-76529df1811a",
            "metadata": {},
            "source": [
                "# Compact Finite Difference Scheme"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b8ced087-d285-4a55-97c6-fc78ad69d186",
            "metadata": {},
            "source": [
                "## Learning Outcomes\n",
                "\n",
                "This examples teaches how to compute derivative of a function using Compact Finite Difference scheme as described in the paper by [Lele](https://www.sciencedirect.com/science/article/abs/pii/002199919290324R).\n",
                "\n",
                "In this example, you will learn:\n",
                "* how to convert stencil expressions from discretization to NumPy slicing operations\n",
                "* how to create a tridiagonal matrix (dense)\n",
                "* how to solve the resulting tridiagonal matrix using `linalg.solve`\n",
                "* how to compute the L2 norm error between exact solution and computed solution using `linalg.norm`\n",
                "\n",
                "Note that a more optimal way of solving tridiagonal matrices is by using the TDMA algorithm. Here, we show how this can be solved using NumPy's `solve` API.\n",
                "\n",
                "---"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "adb9700f-d3d5-4003-a532-123c1c55e340",
            "metadata": {},
            "source": [
                "## Background\n",
                "\n",
                "Compact finite difference schemes approximate the first derivative by including the information of derivative of function at neighboring points in addition to including the value of function themselves, as shown below:\n",
                "\n",
                "$\\alpha f^{'}_{i-1} + f^{'}_{i-1} + \\alpha f^{'}_{i+1} = a_{1} f_{j+1} + a_{2} f_{j+2} - a_{2} f_{j-2} - a_{1} f_{j-1}$\n",
                "\n",
                "This can be represented more compactly in the following form,\\\n",
                "$\\mathbf{A} f' = \\mathbf{B} f$\n",
                "\n",
                "where the matrix $\\mathbf{A}$ is tridiagonal and $\\mathbf{B}$ is pentadiagonal. In this example, we store the matrix, $\\mathbf{A}$, as a dense matrix and explicitly compute $\\mathbf{B} f$ instead of storing B to save memory. To compute the derivative, $f'$, of a function, $f$, using a sixth order compact finite difference, we solve a linear system of equations\n",
                "\n",
                "The main and off-diagonal elements of matrix $\\mathbf{A}$ are 1.0 and 1.0/3 respectively. For this example, we consider a sine function, $f=\\sin({k x})$\n",
                "\n",
                "\n",
                "---"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "df0cdc69-4bf6-4ecd-bb46-4df1b95437c4",
            "metadata": {},
            "source": [
                "The domain extends from 0 to $2 \\pi$ and is discretized using N points. Since the stecil for the RHS extends 2 points on either side, we may have to create arrays of size (N+4) to accomodate storing the values of points outside the domain."
            ]
        },
        {
            "cell_type": "markdown",
            "id": "9475f334-b2ab-4cad-8a1f-8803ecf97371",
            "metadata": {},
            "source": [
                "## Implementation"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "39bc4d10-25e2-4ef8-a70a-2ce270c8eb5f",
            "metadata": {},
            "outputs": [],
            "source": [
                "import numpy as np\n",
                "from matplotlib import pyplot as plt"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "7dc1892b-842f-4200-abf6-769095caa861",
            "metadata": {},
            "outputs": [],
            "source": [
                "# number of points used in discretization\n",
                "npoints: int = 100\n",
                "\n",
                "# number of stencil points to compute the right-hand side\n",
                "n_stencil: int = 2\n",
                "\n",
                "# length of the domain\n",
                "length = 2.0 * np.pi\n",
                "\n",
                "# wavenumber of the initial profile\n",
                "wavenumber = 10"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "ee7c5c26-ffcb-4e6a-acb6-73540e0c735c",
            "metadata": {},
            "outputs": [],
            "source": [
                "# compute the spacing\n",
                "h = length / npoints\n",
                "\n",
                "# generate the discretized points\n",
                "x = np.linspace(0, length, npoints, endpoint=False)\n",
                "\n",
                "# compute the function and exact derivative\n",
                "f_interior = np.sin(wavenumber * x)\n",
                "derivative_exact = wavenumber * np.cos(wavenumber * x)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "17b3c8ce-22dd-4a26-8ec7-ceb51632e59f",
            "metadata": {},
            "outputs": [],
            "source": [
                "# For sixth order, the stencil should be of size 2\n",
                "assert n_stencil == 2"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "816d739e-c4d3-4559-a097-af896eb75228",
            "metadata": {},
            "source": [
                "Compute the function values including the left and right-hand side boundaries"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "59649243-9f42-4344-bbd7-17051d303d1d",
            "metadata": {},
            "outputs": [],
            "source": [
                "function_values = np.zeros(npoints + n_stencil * 2)\n",
                "function_values[n_stencil:-n_stencil] = f_interior\n",
                "\n",
                "# set the RHS boundary values using periodic boundary condition\n",
                "function_values[npoints + n_stencil] = f_interior[0]\n",
                "function_values[npoints + n_stencil + 1] = f_interior[1]\n",
                "\n",
                "# set the LHS boundary values using periodic boundary condition\n",
                "function_values[0] = f_interior[npoints - n_stencil]\n",
                "function_values[1] = f_interior[npoints - n_stencil + 1]"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "3dbc59ce-7099-4f49-ba55-d2f918eb3adc",
            "metadata": {},
            "source": [
                "Form the matrix $\\mathbf{A}$"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "9a11d32c-1568-4a30-aeb6-52480aca34c6",
            "metadata": {},
            "outputs": [],
            "source": [
                "A = np.zeros((npoints, npoints))\n",
                "\n",
                "# Eqn (2.1.7) from Compact Finite Difference with Spectral-like Accuracy, Lele, 1992, JCP.\n",
                "alpha = 1.0 / 3.0\n",
                "\n",
                "# generate the tridiagonal matrix using np.diag\n",
                "main = np.ones((1, npoints))[0]\n",
                "diagonal = alpha * np.ones((1, npoints - 1))[0]\n",
                "A = np.diag(main, 0) + np.diag(diagonal, -1) + np.diag(diagonal, 1)\n",
                "\n",
                "# Apply periodic boundary condition\n",
                "A[0, -1] = alpha\n",
                "A[-1, 0] = alpha"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "204c132c-b4b2-494e-96cc-1b7ee1c55e83",
            "metadata": {},
            "source": [
                "Form the right-hand side"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "d939df3a-6e6c-4e88-b671-18ab0b1e58b9",
            "metadata": {},
            "outputs": [],
            "source": [
                "# Generate the right-hand side\n",
                "a1 = 7.0 / (9.0 * h)\n",
                "a2 = 1.0 / (36.0 * h)\n",
                "\n",
                "# note how $a_{1} f_{j+1} + a_{2} f_{j+2} - a_{2} f_{j-2} - a_{1} f_{j-1}$\n",
                "# gets converted to slicing operations on the array function_values.\n",
                "\n",
                "# It is important to derive the right start and end indices for the slices corresponding to each term.\n",
                "# Since the stencil size on the RHS is 2 (n_stencil), the index j in the equation starts from the second point (j=2)\n",
                "# and ends at j=(npoints+2). This translates to the following slices;\n",
                "\n",
                "# f_{j+2} - f_{j-2} -> (function_values[4:npoints+4] - function_values[0:npoints])\n",
                "# f_{j+1} - f_{j-1} -> (function_values[3:npoints+3] - function_values[1:npoints+1])\n",
                "rhs = np.zeros(npoints)\n",
                "rhs[0:npoints] = a1 * (\n",
                "    function_values[3 : npoints + 3] - function_values[1 : npoints + 1]\n",
                ") + a2 * (function_values[4 : npoints + 4] - function_values[0:npoints])"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "77ee7783-933e-4be0-88c7-984396cdaeef",
            "metadata": {},
            "source": [
                "Compute the derivative and the L2 norm error"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "87df4961-25af-4876-8e46-2762429a3418",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "L2 norm error: 0.0021705301478625403\n"
                    ]
                }
            ],
            "source": [
                "derivative = np.linalg.solve(A, rhs)\n",
                "error = np.linalg.norm(derivative - derivative_exact)\n",
                "print(f\"L2 norm error: {error}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "1015951c-29a9-4be1-b65e-5f3d19efdb9c",
            "metadata": {},
            "source": [
                "---\n",
                "Compare the exact and computed derivative of the function"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "c1d82ac2-d584-451e-8bc7-b618e3a0f9af",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x11403f190>"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.plot(x, f_interior, color=\"red\", label=f\"$f = sin({wavenumber}x)$\")\n",
                "plt.plot(x, derivative, color=\"blue\", label=\"Computed $f'(x)$\")\n",
                "plt.plot(x, derivative_exact, color=\"green\", label=\"Exact f'(x)\")\n",
                "\n",
                "plt.xlabel(\"x\")\n",
                "plt.ylabel(\"$f(x), f'(x)$\")\n",
                "plt.title(\n",
                "    \"Compute derivative of a function using sixth-order accurate compact finite difference scheme\"\n",
                ")\n",
                "plt.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5))"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "4f05eea3-5e1e-4567-9d7a-6ef8143f2723",
            "metadata": {},
            "source": [
                "We see that the curves for the computed and the exact derivative overlap (green and blue) and the L2 error is of the order of 1e-3. If you are curious,\n",
                "\n",
                "* Try increasing the wavenumber, $k$, to see how this affects the accuracy of the solution. Plot error vs wavenumber to see how it varies\n",
                "* Try reducing the resolution of the grid (n_points) and plot the error vs number of points.\n",
                "* Try plotting the modified wavenumber and see if you can match the curve in the paper by [Lele](https://www.sciencedirect.com/science/article/abs/pii/002199919290324R)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "9573de0a-be2a-4781-ae79-3ca9dcb17f05",
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "python3 (legate) *",
            "language": "python",
            "name": "conda-env-legate-py"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.10.13"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}
